Chest computed tomography radiomics to predict the outcome for patients with COVID-19 at an early stage

PURPOSE: Early monitoring and intervention for patients with novel coronavirus disease-2019 (COVID-19) will benefit both patients and the medical system. Chest computed tomography (CT) radiomics provide more information regarding the prognosis of COVID-19. METHODS: A total of 833 quantitative features of 157 COVID-19 patients in the hospital were extracted. By filtering unstable features using the least absolute shrinkage and selection operator algorithm, a radiomic signature was built to predict the prognosis of COVID-19 pneumonia. The main outcomes were the area under the curve (AUC) of the prediction models for death, clinical stage, and complications. Internal validation was performed using the bootstrapping validation technique. RESULTS: The AUC of each model demonstrated good predictive accuracy [death, 0.846; stage, 0.918; complication, 0.919; acute respiratory distress syndrome (ARDS), 0.852]. After finding the optimal cut-off for each outcome, the respective accuracy, sensitivity, and specificity were 0.854, 0.700, and 0.864 for the prediction of the death of COVID-19 patients; 0.814, 0.949, and 0.732 for the prediction of a higher stage of COVID-19; 0.846, 0.920, and 0.832 for the prediction of complications of COVID-19 patients; and 0.814, 0.818, and 0.814 for ARDS of COVID-19 patients. The AUCs after bootstrapping were 0.846 [95% confidence interval (CI): 0.844-0.848] for the death prediction model, 0.919 (95% CI: 0.917-0.922) for the stage prediction model, 0.919 (95% CI: 0.916-0.921) for the complication prediction model, and 0.853 (95% CI: 0.852-0.0.855) for the ARDS prediction model in the internal validation. Based on the decision curve analysis, the radiomics nomogram was clinically significant and useful. CONCLUSION: The radiomic signature from the chest CT was significantly associated with the prognosis of COVID-19. A radiomic signature model achieved maximum accuracy in the prognosis prediction. Although our results provide vital insights into the prognosis of COVID-19, they need to be verified by large samples in multiple centers.

Host protection against Omicron BA.2.2 sublineages by prior vaccination in spring 2022 COVID-19 outbreak in Shanghai.

The Omicron family of SARS-CoV-2 variants are currently driving the COVID-19 pandemic. Here we analyzed the clinical laboratory test results of 9911 Omicron BA.2.2 sublineages-infected symptomatic patients without earlier infection histories during a SARS-CoV-2 outbreak in Shanghai in spring 2022. Compared to an earlier patient cohort infected by SARS-CoV-2 prototype strains in 2020, BA.2.2 infection led to distinct fluctuations of pathophysiological markers in the peripheral blood. In particular, severe/critical cases of COVID-19 post BA.2.2 infection were associated with less pro-inflammatory macrophage activation and stronger interferon alpha response in the bronchoalveolar microenvironment. Importantly, the abnormal biomarkers were significantly subdued in individuals who had been immunized by 2 or 3 doses of SARS-CoV-2 prototype-inactivated vaccines, supporting the estimation of an overall 96.02% of protection rate against severe/critical disease in the 4854 cases in our BA.2.2 patient cohort with traceable vaccination records. Furthermore, even though age was a critical risk factor of the severity of COVID-19 post BA.2.2 infection, vaccination-elicited protection against severe/critical COVID-19 reached 90.15% in patients aged ≽ 60 years old. Together, our study delineates the pathophysiological features of Omicron BA.2.2 sublineages and demonstrates significant protection conferred by prior prototype-based inactivated vaccines.

Primary assessment of the diversity of Omicron sublineages and the epidemiologic features of autumn/winter 2022 COVID-19 wave in Chinese mainland.

With the recent ongoing autumn/winter 2022 COVID-19 wave and the adjustment of public health control measures, there have been widespread SARS-CoV-2 infections in Chinese mainland. Here we have analyzed 369 viral genomes from recently diagnosed COVID-19 patients in Shanghai, identifying a large number of sublineages of the SARS-CoV-2 Omicron family. Phylogenetic analysis, coupled with contact history tracing, revealed simultaneous community transmission of two Omicron sublineages dominating the infections in some areas of China (BA.5.2 mainly in Guangzhou and Shanghai, and BF.7 mainly in Beijing) and two highly infectious sublineages recently imported from abroad (XBB and BQ.1). Publicly available data from August 31 to November 29, 2022 indicated an overall severe/critical case rate of 0.035% nationwide, while analysis of 5706 symptomatic patients treated at the Shanghai Public Health Center between September 1 and December 26, 2022 showed that 20 cases (0.35%) without comorbidities progressed into severe/critical conditions and 153 cases (2.68%) with COVID-19-exacerbated comorbidities progressed into severe/critical conditions. These observations shall alert healthcare providers to place more resources for the treatment of severe/critical cases. Furthermore, mathematical modeling predicts this autumn/winter wave might pass through major cities in China by the end of the year, whereas some middle and western provinces and rural areas would be hit by the upcoming infection wave in mid-to-late January 2023, and the duration and magnitude of upcoming outbreak could be dramatically enhanced by the extensive travels during the Spring Festival (January 21, 2023). Altogether, these preliminary data highlight the needs to allocate resources to early diagnosis and effective treatment of severe cases and the protection of vulnerable population, especially in the rural areas, to ensure the country's smooth exit from the ongoing pandemic and accelerate socio-economic recovery.

Nirmatrelvir-ritonavir therapy and COVID-19 vaccination improve clinical outcomes of SARS-CoV-2 Omicron variant infection.

To evaluate the effect of Nirmatrelvir-ritonavir therapy and coronavirus disease 2019 (COVID-19) vaccination on clinical outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron infection, we retrospectively analyzed the clinical data of 762 adult patients with confirmed Omicron BA2.2 variant infection, of them 488 patients received standard therapy and 274 patients received Nirmatrelvir-ritonavir therapy. Subjects were matched by propensity score matching using R language, the baseline factors were balanced by the nearest-neighbor matching method and were compared, together with the factors including progression to severe/critical disease, viral clearance time, length of hospital stay, and virological rebound of SARS-CoV-2 infection. Nirmatrelvir-ritonavir therapy significantly accelerated viral clearance at Days 14 and  28 during hospitalization, but it had no impact on disease progression, length of hospital stay, or infection rebound. In contrast, COVID-19 vaccination before admission was positively correlated with the viral clearance rate and negatively correlated with disease progression in a dose-dependent way. COVID-19 vaccination reduced the probability of infection rebound. Other factors such as the number of comorbidities, pneumonia on-admission, and high D2 levels were positively correlated with disease progression. Our study strongly recommended booster COVID-19 vaccination for the elderly population, particularly patients with comorbidities to prevent critical disease.

Albumin Infusion May Improve the Prognosis of Critical COVID-19 Patients with Hypoalbuminemia in the Intensive Care Unit: A Retrospective Cohort Study.

Background: The coronavirus disease 2019 (COVID-19) pandemic has caused enormous mortality worldwide. Low albumin level is a risk factor for increasing mortality among patients in the intensive care unit (ICU). This study investigated the effect of albumin infusion on critical COVID-19 patients with hypoalbuminemia. Methods: A total of 114 COVID-19 ICU patients with hypoalbuminemia were recruited from Wuhan Leishenshan Hospital and Zhongnan Hospital of Wuhan University. Clinical features and laboratory variables were collected through electronic medical records. The cohorts were divided into two groups: albumin infusion and non-albumin infusion. Propensity-matched analysis was used to compare patients who received albumin to controls. Statistical analyses were used to investigate the survival time and inflammation-related blood biomarkers between groups. Results: Lactate dehydrogenase, interleukin (IL)-6, IL-2 receptor, and IL-8 levels were significantly downregulated in the albumin infusion group. Significant upregulations of lymphocyte counts and IL-10 were found in the albumin infusion group. There was a negative association between albumin level and D-dimer or procalcitonin levels after treatment. The albumin infusion group had a significantly longer survival time and shorter hospitalization time than control patients. Notably, a 1g increase in albumin level reduced the risk of death by approximately 7.3% after adjusting for age and sex. Patients with increased albumin levels after treatment had better prognoses than those without. Conclusion: Albumin administration can regulate COVID-19-related biomarkers and reduce the risk of death in critical patients with hypoalbuminemia. Clinicians should pay more attention to these risk factors. Targeted clinical interventions should be implemented to minimize the negative impacts of hypoalbuminemia and improve disease outcomes.

Discussion on China's anti-epidemic response based on the Protocol on Prevention and Control of Coronavirus Disease 2019 from Chinese Authority.

Eight versions of the Protocol on Prevention and Control of Coronavirus Disease 2019 (COVID-19) (the Protocol) were issued successively by the Chinese authority to guide the local responses since the first COVID-19 case appeared in Wuhan, China. This study aimed to investigate the evolution of the overall strategy and specific measures in these Protocols, and several recommendations were provided after analysing China's response to the epidemic resurgence. As a result, we found a gradual expanding trend in case surveillance, early screening, and epidemiological investigation, as well as a progressively rigorous tendency in isolation measures and close contact management. With the Protocol's guidance, China had achieved success in several recent fights against domestic COVID-19 resurgences. The city lockdown and multiple city-wide nucleic acid tests adopted were deemed necessary in COVID-19 resurgence's battle. Besides, the large-scale distance centralised quarantine, which is, quarantine in a purpose-built isolation station away from communities where people under quarantine lived, was promoted in rural areas. China's anti-epidemic achievements provide ideas for the global battle against COVID-19.

Effectiveness of remote continuous glucose monitoring on adverse outcomes among patients with diabetes complicated with COVID-19.

This prospective study provided an effective way of glucose monitoring for patients with diabetes complicated with coronavirus disease 2019. The use of an intermittently scanned continuous glucose monitoring system was significantly associated with better outcomes of coronavirus disease 2019 in patients with pre-existing diabetes.

Glycemic fluctuations caused by COVID-19: Results from continuous glucose monitoring.

AIM: We aimed to address the potential impact of COVID-19 on glycemic patterns in a small pilot study. METHOD: 13 patients with mild COVID-19 who were confirmed without diabetes and another group of 18 healthy individuals with available CGM data were well matched and enrolled into the final analysis. RESULTS: We noticed significantly higher TARs of >140 mg/dL (median 13.9% vs. 2.3%, P = 0.006), >160 mg/dL (median 4.7% vs. 0.0%, P = 0.011) and >180 mg/dL (median 1.9% vs. 0.0%, P = 0.007) among non-diabetic patients with COVID-19 than those among healthy individuals. There was no significant difference in TBR of <70 mg/dL or <54 mg/dL (all P > 0.1). Consequently, the TIR of 70 mg/dL to 140 mg/dL was significantly lower in non-diabetic patients with COVID-19 than that in healthy individuals (median 80.1% vs. 93.1%, P = 0.001). Significant postprandial glycemic fluctuations were observed among patients with COVID-19. There was a remarkable difference in CV in non-diabetic patients with COVID-19 compared to healthy individuals (median 25.6% vs. 15.7%, P < 0.001). CONCLUSION: Significant higher glycemic fluctuation and exposure to hyperglycemia was associated with COVID-19 among previously normoglycemic individuals, characterized with potentially impaired glucose tolerance.

Thresholds of Glycemia and the Outcomes of COVID-19 Complicated With Diabetes: A Retrospective Exploratory Study Using Continuous Glucose Monitoring.

OBJECTIVE: Although elevated glucose levels are reported to be associated with adverse outcomes of coronavirus disease 2019 (COVID-19), the optimal range of glucose in patients with COVID-19 and diabetes remains unknown. This study aimed to investigate the threshold of glycemia and its association with the outcomes of COVID-19. RESEARCH DESIGN AND METHODS: Glucose levels were assessed through intermittently scanned continuous glucose monitoring in 35 patients for an average period of 10.2 days. The percentages of time above range (TAR), time below range (TBR), time in range (TIR), and coefficient of variation (CV) were calculated. Composite adverse outcomes were defined as either the need for admission to the intensive care unit, need for mechanical ventilation, or morbidity with critical illness. RESULTS: TARs using thresholds from 160 to 200 mg/dL were significantly associated with composite adverse outcomes after adjustment of covariates. Both TBR (<70 mg/dL) and TIR (70-160 mg/dL), but not mean sensor glucose level, were significantly associated with composite adverse outcomes and prolonged hospitalization. The multivariate-adjusted odds ratios of the CV of sensor glucose across tertiles for composite adverse outcomes of COVID-19 were 1.00, 1.18, and 25.2, respectively. CONCLUSIONS: Patients with diabetes and COVID-19 have an increased risk of adverse outcomes with glucose levels >160 mg/dL and <70 mg/dL and a high CV. Therapies that improve these metrics of glycemic control may result in better prognoses for these patients.

Incorporation of Urinary Neutrophil Gelatinase-Associated Lipocalin and Computed Tomography Quantification to Predict Acute Kidney Injury and In-Hospital Death in COVID-19 Patients.

BACKGROUND: The prevalence of acute kidney injury (AKI) in COVID-19 patients is high, with poor prognosis. Early identification of COVID-19 patients who are at risk for AKI and may develop critical illness and death is of great importance. OBJECTIVE: The aim of this study was to develop and validate a prognostic model of AKI and in-hospital death in patients with COVID-19, incorporating the new tubular injury biomarker urinary neutrophil gelatinase-associated lipocalin (u-NGAL) and artificial intelligence (AI)-based chest computed tomography (CT) analysis. METHODS: A single-center cohort of patients with COVID-19 from Wuhan Leishenshan Hospital were included in this study. Demographic characteristics, laboratory findings, and AI-assisted chest CT imaging variables identified on hospital admission were screened using least absolute shrinkage and selection operator (LASSO) and logistic regression to develop a model for predicting the AKI risk. The accuracy of the AKI prediction model was measured using the concordance index (C-index), and the internal validity of the model was assessed by bootstrap resampling. A multivariate Cox regression model and Kaplan-Meier curves were analyzed for survival analysis in COVID-19 patients. RESULTS: One hundred seventy-four patients were included. The median (±SD) age of the patients was 63.59 ± 13.79 years, and 83 (47.7%) were men.u-NGAL, serum creatinine, serum uric acid, and CT ground-glass opacity (GGO) volume were independent predictors of AKI, and all were selected in the nomogram. The prediction model was validated by internal bootstrapping resampling, showing results similar to those obtained from the original samples (i.e., 0.958; 95% CI 0.9097-0.9864). The C-index for predicting AKI was 0.955 (95% CI 0.916-0.995). Multivariate Cox proportional hazards regression confirmed that a high u-NGAL level, an increased GGO volume, and lymphopenia are strong predictors of a poor prognosis and a high risk of in-hospital death. CONCLUSIONS: This model provides a useful individualized risk estimate of AKI in patients with COVID-19. Measurement of u-NGAL and AI-based chest CT quantification are worthy of application and may help clinicians to identify patients with a poor prognosis in COVID-19 at an early stage.